webheart

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Inspiration: While staying overnight in the hospital during my grandmother’s post-surgery recovery, I spent hours listening to the steady beeps of a bulky heart monitor. I kept wondering — why does something so essential need so many wires, take up so much space, and consume so much electricity? In many rural areas, people don’t even have access to such equipment. That curiosity led me to explore whether heart health monitoring could be made simpler, more accessible, and more affordable. My research into photoplethysmography (PPG) and quantum computing sparked the idea for a new, more efficient solution.

What it does: WebHeart is a quantum-enhanced heart health screening tool that uses a standard webcam to detect early signs of cardiovascular risk. It captures subtle changes in skin color caused by blood flow (PPG signals), cleans and isolates them using a Quantum Fourier Transform (QFT), and classifies risk patterns using Quantum Support Vector Machines (QSVM). All processing is optimized with Classiq to run in near real time, even on consumer hardware. The result: hospital-grade risk assessment anywhere — no ECG machines, no specialized sensors, just a camera and quantum-powered algorithms.

How I built it: I started by creating a pipeline to capture video from a standard webcam and extract PPG waveforms. I implemented QFT for noise filtering to remove motion artifacts, lighting variations, and sensor noise. For classification, I encoded extracted features into a quantum state and trained a QSVM to separate healthy and at-risk patterns. I used Classiq to optimize the circuits for minimal qubits and depth, balancing performance between 4-qubit (fast, NISQ-friendly) and 8-qubit (higher resolution) designs. I tested the system on simulated signals based on MIT-BIH Arrhythmia datasets to verify accuracy and stability.

Challenges I ran into: One major challenge was extracting reliable PPG signals from a webcam feed, which is prone to noise from lighting, motion, and camera quality. Integrating quantum algorithms in a way that genuinely improved signal quality — rather than slowing it down — required careful optimization. Circuit depth had to be minimized to make it NISQ-friendly, which meant tuning the QFT and QSVM implementations to run efficiently. Another hurdle was the lack of a large dataset of raw webcam PPG recordings, so I used synthetic and medical research datasets for validation.

Accomplishments that I’m proud of: I successfully demonstrated that a standard webcam, combined with quantum algorithms, can produce clean, reliable heart-rate variability data for risk classification. I optimized the quantum circuits with Classiq to make them fast enough for potential real-time use, which is rare for current QML implementations. Most importantly, I created a system that could be deployed anywhere — from hospitals to remote clinics — without the need for expensive hardware. Turning a personal moment of curiosity into a tangible, functional prototype is my proudest achievement here.

What I learned: I learned how quantum algorithms like QFT and QSVM can have practical, real-world applications beyond academic demonstrations, especially in processing noisy biological signals. I also deepened my understanding of how to balance circuit complexity with NISQ constraints using Classiq’s optimization tools. This project reinforced that accessibility in healthcare isn’t just about lowering cost — it’s about making technology work in the environments where it’s needed most, even when resources are minimal.

What’s next for WebHeart: My next step is to test WebHeart with real webcam PPG data in different environments — varied lighting, skin tones, and motion conditions — to ensure robustness. I aim to integrate the system into a simple web or mobile app so healthcare workers and individuals can run screenings instantly. I’m also exploring hybrid quantum-classical models to further improve speed and accuracy, as well as integrating the system into wearable devices. Ultimately, my goal is to make WebHeart a scalable, globally accessible tool for preventive heart health monitoring.

Built With

• classiq
• powerpoint